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ISSTA2025顶会

Type-Alias Analysis: Enabling LLVM IR with Accurate Types

Jinmeng Zhou, Ziyue Pan, Wenbo Shen, Xingkai Wang, Kangjie Lu, Zhiyun Qian

2025年份
1被引次数
1顶会引用

摘要

LLVM Intermediate Representation (IR) underpins the LLVM compiler infrastructure, offering a strong type system and a static single-assignment (SSA) form that are well-suited for program analysis. However, its single-type design assigns exactly one type to each IR variable, even when the variable may legitimately correspond to multiple types. The recent introduction of opaque pointers exacerbates this limitation: all pointers in the IR are uniformly represented with a generic pointer type (ptr) that erases concrete pointee type information, making many type-based analyses ineffective. To address the limitations of single-type design, we introduce type-alias analysis, a multiple-type design that maintains type-alias sets for IR variables and infers types across IR instructions. We have developed TypeCopilot, a prototype that recovers concrete pointee types for opaque-pointer-enabled LLVM IR generated from C programs. TypeCopilot achieves 98.57% accuracy with 94.98% coverage, allowing existing analysis tools to retain their effectiveness despite the adoption of opaque pointers. To foster further research and security applications, we have open-sourced TypeCopilot, providing the community with a practical foundation for precise, type-aware security analyses on modern LLVM IR. CCS Concepts: • Software and its engineering → Software notations and tools.

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